VCs double down on data-driven investment models

New efforts could automate the role of an analyst

Social Capital co-founder Chamath Palihapitiya is spinning out a company from his venture capital fund-turned-family-office, TechCrunch has learned. The new entity, temporarily dubbed CaaS (short for capital-as-a-service) Technologies, will focus on providing data-driven insights to VC firms.

Data informs investment decisions at VC funds more than ever, as new technologies make way for increasingly quantitative approaches to deal-making. But when it comes to third-party data analysis tools, there are few options tailored to VCs.

Palihapitiya’s latest effort will operate as a standalone business, automating the time-sucking process of evaluating a company’s health prior to investing. Zafer Younis, former partner at San Francisco accelerator 500 Startups, has been named CEO of the business, which is expected to launch this fall.

Palihapitiya did not respond to a request for comment. Younis could not be reached for comment.

According to Younis’ LinkedIn profile, which indicates he spent nine months at Social Capital in 2018, CaaS Technologies is “a collection of quantitative diligence tools developed to help VCs evaluate investment opportunities and make better data-driven decisions. CaaS reduces diligence time and offers investors insights that are otherwise a burden to the founder and investment team to process and prepare. Founders are using CaaS to improve their pitches and drive investor conviction using transparent and defendable data.”

CaaS Technologies’ approach resembles that of Social Capital’s “magic 8-ball,” a quantitative tool for due diligence built by former Social Capital partner Jonathan Hsu several years ago. The goal of 8-ball was to develop a standardized method of determining product-market fit in early-stage startups. In 2016, Social Capital decided to open-source 8-ball, granting startups access to its basic features.

Palihapitiya is choosing to monetize Social Capital’s IP shortly after Tribe Capital, a relatively new fund managed by a trio of former Social Capital data wizards including Hsu, began investing in startups using 8-ball’s methodology.

Hsu declined to comment for this story.

In addition to hiring Younis, CaaS Technologies has formed a small team complete with engineers, raised capital and formed relationships with more than a dozen institutional venture funds, sources tell TechCrunch. We have not yet identified any of the venture funds working with CaaS Technologies.

Co-founder Social Capital, Chamath Palihapitiya, speaks onstage during “The State of the Valley: Where’s the Juice?” at the Vanity Fair New Establishment Summit at Yerba Buena Center for the Arts on October 19, 2016 in San Francisco, California. (Photo by Michael Kovac/Getty Images for Vanity Fair)

‘Craftsman-at-scale’

Lightspeed Venture Partners’ Brad Twohig said he wasn’t aware of CaaS Technologies efforts to team with VCs, rather, LSVP has opted to develop a data science team in-house.

Twohig declined to disclose the size of LSVP’s data-focused team; a representative for LSVP said the size and scale of the team is part of the firm’s “secret sauce.”

“You have to strike a balance between being well-informed people with a data advantage by using all the tools and software while avoiding the temptation to go too far,” Twohig tells TechCrunch. “At the end of the day, this is still something where we are looking to take a craftsman-at-scale approach to our investing as opposed to just ‘hey, we’ve got an algorithm and it’s gonna spit out whether we fund you or not.'”

“When people are building stealth-fighter jets, they are handcrafted, they are highly informed by data and architectural drawings but they are still hand built with a lot of precision,” he added.

As data insights become an integral part of the diligence process for startup investing, firms like LSVP are tapping new talent, developing data-first investment theses or establishing funds reliant on algorithms. Tribe Capital recently launched with a data-supported strategy, for example. EQT Ventures touts the success of its machine learning system Motherbrain, claiming the algorithm can identify future unicorns.

‘Augmentation of an analyst’

TruValue Labs, a startup headquartered in San Francisco, offers a third-party data analysis platform to Wall Street investors. The company sells a subscription-based AI product to investment managers at hedge and private equity funds, helping them lower the risk profile of a given investment by better understanding the health of a business using thousands of unstructured data sources.

“There’s a huge spur from large asset managers trying to build tools themselves using ML tech and AI but can all asset managers attract engineering talent to do this themselves? Absolutely not.” TruValue co-founder and CEO Hendrik Bartel tells TechCrunch. “I don’t think all asset managers have it in them to become a software company. I’ve seen more and more third party platforms come out of nowhere.”

TruValue focuses on evaluating public market investment opportunities on the basis of environmental, social and corporate governance (ESG) issues. Recently, it’s seen a greater demand for transparency in the private markets.

“Private equity investors want to have greater transparency into their investments, and from a due diligence perspective, they want to know more about these companies before they invest in them,” Bartel said.

Bartel refers to his approach — and that of CaaS Technologies — as “an augmentation of an analyst.” At venture capital firms, analysts are often charged with researching businesses and perusing available business and financial records to help a firm decide whether to move forward with a startup.

“It’s virtually impossible for an analyst or an asset manager to cover all the companies in its portfolio,” Bartel said. “To read all the information about a publicly held company, it would take an analyst six years.”

Ultimately, leveraging a thoughtful tool and the expertise of an experienced team may make a lot more sense for a VC firm than building out their own data science teams. Not only are data scientists costly and competitive, but data scientists well-versed in the venture capital asset class are fewer and farther between.

As for CaaS Technologies specifically, an attempt to monetize the features that made Social Capital one of the top venture capital funds, albeit for a short time period, is a logical path forward for the team.